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Roope Astala
2018-09-14 15:14:43 -04:00
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 01. Train in the Notebook & Deploy Model to ACI\n",
"\n",
"* Load workspace\n",
"* Train a simple regression model directly in the Notebook python kernel\n",
"* Record run history\n",
"* Find the best model in run history and download it.\n",
"* Deploy the model as an Azure Container Instance (ACI)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"1. Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't. \n",
"\n",
"2. Install following pre-requisite libraries to your conda environment and restart notebook.\n",
"```shell\n",
"(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
"```\n",
"\n",
"3. Check that ACI is registered for your Azure Subscription. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!az provider show -n Microsoft.ContainerInstance -o table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If ACI is not registered, run following command to register it. Note that you have to be a subscription owner, or this command will fail."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!az provider register -n Microsoft.ContainerInstance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validate Azure ML SDK installation and get version number for debugging purposes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"install"
]
},
"outputs": [],
"source": [
"from azureml.core import Experiment, Run, Workspace\n",
"import azureml.core\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set experiment name\n",
"Choose a name for experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'train-in-notebook'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a training run in local Notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load diabetes dataset, a well-known small dataset that comes with scikit-learn\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.externals import joblib\n",
"\n",
"X, y = load_diabetes(return_X_y = True)\n",
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
"data = {\n",
" \"train\":{\"X\": X_train, \"y\": y_train}, \n",
" \"test\":{\"X\": X_test, \"y\": y_test}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train a simple Ridge model\n",
"Train a very simple Ridge regression model in scikit-learn, and save it as a pickle file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reg = Ridge(alpha = 0.03)\n",
"reg.fit(data['train']['X'], data['train']['y'])\n",
"preds = reg.predict(data['test']['X'])\n",
"print('Mean Squared Error is', mean_squared_error(preds, data['test']['y']))\n",
"joblib.dump(value = reg, filename = 'model.pkl');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add experiment tracking\n",
"Now, let's add Azure ML experiment logging, and upload persisted model into run record as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"local run",
"outputs upload"
]
},
"outputs": [],
"source": [
"experiment = Experiment(workspace = ws, name = experiment_name)\n",
"run = experiment.start_logging()\n",
"run.tag(\"Description\",\"My first run!\")\n",
"run.log('alpha', 0.03)\n",
"reg = Ridge(alpha = 0.03)\n",
"reg.fit(data['train']['X'], data['train']['y'])\n",
"preds = reg.predict(data['test']['X'])\n",
"run.log('mse', mean_squared_error(preds, data['test']['y']))\n",
"joblib.dump(value = reg, filename = 'model.pkl')\n",
"run.upload_file(name = 'outputs/model.pkl', path_or_stream = './model.pkl')\n",
"\n",
"run.complete()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can browse to the recorded run. Please make sure you use Chrome to navigate the run history page."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple parameter sweep\n",
"Sweep over alpha values of a sklearn ridge model, and capture metrics and trained model in the Azure ML experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"local run",
"outputs upload"
]
},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"from tqdm import tqdm\n",
"\n",
"model_name = \"model.pkl\"\n",
"\n",
"# start a training run\n",
"root_run = experiment.start_logging()\n",
"\n",
"# list of numbers from 0 to 1.0 with a 0.05 interval\n",
"alphas = np.arange(0.0, 1.0, 0.05)\n",
"\n",
"# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
"for alpha in tqdm(alphas):\n",
" # create a bunch of child runs\n",
" with root_run.child_run(\"alpha-\" + str(alpha)) as run:\n",
" # Use Ridge algorithm to build a regression model\n",
" reg = Ridge(alpha=alpha)\n",
" reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n",
" preds = reg.predict(data[\"test\"][\"X\"])\n",
" mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
"\n",
" # log alpha, mean_squared_error and feature names in run history\n",
" run.log(\"alpha\", alpha)\n",
" run.log(\"mse\", mse)\n",
" run.log_list(\"columns\", columns)\n",
"\n",
" with open(model_name, \"wb\") as file:\n",
" joblib.dump(value=reg, filename=file)\n",
" \n",
" # upload the serialized model into run history record\n",
" run.upload_file(name=\"outputs/\" + model_name, path_or_stream=model_name)\n",
"\n",
" # now delete the serialized model from local folder since it is already uploaded to run history \n",
" os.remove(model_name)\n",
" \n",
"# Declare run completed\n",
"root_run.complete()\n",
"root_run_id = root_run.id\n",
"print (\"run id:\", root_run.id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can reconstruct this run object from captured run id in a different Notebook session."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history"
]
},
"outputs": [],
"source": [
"rr = Run(experiment=experiment, run_id=root_run_id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select best model from the experiment\n",
"Load all child run metrics recursively from the experiment into a dictionary object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history",
"get metrics"
]
},
"outputs": [],
"source": [
"child_run_metrics = rr.get_metrics(recursive=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now find the run with the lowest Mean Squared Error value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run_id = min(child_run_metrics, key = lambda k: child_run_metrics[k]['mse'])\n",
"best_run = Run(experiment=experiment, run_id=best_run_id)\n",
"print('Best run is:', best_run_id)\n",
"print('Metrics:', child_run_metrics[best_run_id])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can add tags to your runs to make them easier to catalog"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history"
]
},
"outputs": [],
"source": [
"best_run.tag(key=\"Description\", value=\"The best one\")\n",
"best_run.get_tags()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot MSE over alpha\n",
"\n",
"Let's observe the best model visually by plotting the MSE values over alpha values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"best_alpha = child_run_metrics[best_run_id]['alpha']\n",
"min_mse = child_run_metrics[best_run_id]['mse']\n",
"\n",
"alpha_mse = np.array([(child_run_metrics[k]['alpha'], child_run_metrics[k]['mse']) for k in child_run_metrics.keys()])\n",
"sorted_alpha_mse = alpha_mse[alpha_mse[:,0].argsort()]\n",
"\n",
"plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'r--')\n",
"plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'bo')\n",
"\n",
"plt.xlabel('alpha', fontsize = 14)\n",
"plt.ylabel('mean squared error', fontsize = 14)\n",
"plt.title('MSE over alpha', fontsize = 16)\n",
"\n",
"# plot arrow\n",
"plt.arrow(x = best_alpha, y = min_mse + 39, dx = 0, dy = -26, ls = '-', lw = 0.4,\n",
" width = 0, head_width = .03, head_length = 8)\n",
"\n",
"# plot \"best run\" text\n",
"plt.text(x = best_alpha - 0.08, y = min_mse + 50, s = 'Best Run', fontsize = 14)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register the best model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the model file saved in the run record of best run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history"
]
},
"outputs": [],
"source": [
"for f in best_run.get_file_names():\n",
" print(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can register this model in the model registry of the workspace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from history"
]
},
"outputs": [],
"source": [
"model = best_run.register_model(model_name='best_model', model_path='outputs/model.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify that the model has been registered properly. If you have done this several times you'd see the version number auto-increases each time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from history"
]
},
"outputs": [],
"source": [
"for m in ws.models(name='best_model'):\n",
" print(m.name, m.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also download the registered model. Afterwards, you should see a `model.pkl` file in the current directory. You can then use it for local testing if you'd like."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"download file"
]
},
"outputs": [],
"source": [
"model.download(target_dir='.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create scoring script\n",
"\n",
"The scoring script consists of two functions: `init` that is used to load the model to memory when starting the container, and `run` that makes the prediction when web service is called."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `%%writefile` cell magic is used to write the scoring function to a local file. Pay special attention to how the model is loaded in the `init()` function. When Docker image is built for this model, the actual model file is downloaded and placed on disk, and `get_model_path` function returns the local path where the model is placed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy as np\n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"\n",
"\n",
"def init():\n",
" global model\n",
" # note here \"best_model\" is the name of the model registered under the workspace\n",
" # this call should return the path to the model.pkl file on the local disk.\n",
" model_path = Model.get_model_path(model_name='best_model')\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
" \n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = np.array(data)\n",
" result = model.predict(data)\n",
" return json.dumps({\"result\": result.tolist()})\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"error\": result})\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create conda dependency file\n",
"\n",
"This `myenv.yml` file is used to specify which library dependencies to install on the web service. Note that the CondaDependencies API automatically adds necessary Azure ML dependencies."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies()\n",
"myenv.add_conda_package(\"scikit-learn\")\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View the `myenv.yml` file written."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pfile myenv.yml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy web service into an Azure Container Instance\n",
"The deployment process takes the registered model and your scoring scrip, and builds a Docker image. It then deploys the Docker image into Azure Container Instance as a running container with an HTTP endpoint readying for scoring calls. Read more about [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/).\n",
"\n",
"Note ACI is great for quick and cost-effective dev/test deployment scenarios. For production workloads, please use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Please follow in struction in [this notebook](11.production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n",
" \n",
"** Note: ** The web service creation can take 6-7 minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" tags={'sample name': 'AML 101'}, \n",
" description='This is a great example.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the below `WebService.deploy_from_model()` function takes a model object registered under the workspace. It then bakes the model file in the Docker image so it can be looked-up using the `Model.get_model_path()` function in `score.py`. \n",
"\n",
"If you have a local model file instead of a registered model object, you can also use the `WebService.deploy()` function which would register the model and then deploy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"image_config = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
" runtime=\"python\", \n",
" conda_file=\"myenv.yml\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"%%time\n",
"# this will take 5-10 minutes to finish\n",
"# you can also use \"az container list\" command to find the ACI being deployed\n",
"service = Webservice.deploy_from_model(name='my-aci-svc',\n",
" deployment_config=aciconfig,\n",
" models=[model],\n",
" image_config=image_config,\n",
" workspace=ws)\n",
"\n",
"service.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Test web service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"print('web service is hosted in ACI:', service.scoring_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the `run` API to call the web service with one row of data to get a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"import json\n",
"# score the first row from the test set.\n",
"test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
"service.run(input_data = test_samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feed the entire test set and calculate the errors (residual values)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"# score the entire test set.\n",
"test_samples = json.dumps({'data': X_test.tolist()})\n",
"\n",
"result = json.loads(service.run(input_data = test_samples))['result']\n",
"residual = result - y_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also send raw HTTP request to test the web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"import requests\n",
"import json\n",
"\n",
"# 2 rows of input data, each with 10 made-up numerical features\n",
"input_data = \"{\\\"data\\\": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\"\n",
"\n",
"headers = {'Content-Type':'application/json'}\n",
"\n",
"# for AKS deployment you'd need to the service key in the header as well\n",
"# api_key = service.get_key()\n",
"# headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)} \n",
"\n",
"resp = requests.post(service.scoring_uri, input_data, headers = headers)\n",
"print(resp.text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Residual graph\n",
"Plot a residual value graph to chart the errors on the entire test set. Observe the nice bell curve."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios':[3, 1], 'wspace':0, 'hspace': 0})\n",
"f.suptitle('Residual Values', fontsize = 18)\n",
"\n",
"f.set_figheight(6)\n",
"f.set_figwidth(14)\n",
"\n",
"a0.plot(residual, 'bo', alpha=0.4);\n",
"a0.plot([0,90], [0,0], 'r', lw=2)\n",
"a0.set_ylabel('residue values', fontsize=14)\n",
"a0.set_xlabel('test data set', fontsize=14)\n",
"\n",
"a1.hist(residual, orientation='horizontal', color='blue', bins=10, histtype='step');\n",
"a1.hist(residual, orientation='horizontal', color='blue', alpha=0.2, bins=10);\n",
"a1.set_yticklabels([])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Delete ACI to clean up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Deleting ACI is super fast!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"%%time\n",
"service.delete()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

432
00.Getting Started/02.train-on-local/02.train-on-local.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 02. Train locally\n",
"* Create or load workspace.\n",
"* Create scripts locally.\n",
"* Create `train.py` in a folder, along with a `my.lib` file.\n",
"* Configure & execute a local run in a user-managed Python environment.\n",
"* Configure & execute a local run in a system-managed Python environment.\n",
"* Configure & execute a local run in a Docker environment.\n",
"* Query run metrics to find the best model\n",
"* Register model for operationalization."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.workspace import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create An Experiment\n",
"**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"experiment_name = 'train-on-local'\n",
"exp = Experiment(workspace=ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a folder to store the training script."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"script_folder = './samples/train-on-local'\n",
"os.makedirs(script_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create `train.py`\n",
"\n",
"Use `%%writefile` magic to write training code to `train.py` file under your script folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $script_folder/train.py\n",
"\n",
"import os\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"from azureml.core.run import Run\n",
"from sklearn.externals import joblib\n",
"\n",
"# example of referencing another script\n",
"import mylib\n",
"\n",
"X, y = load_diabetes(return_X_y=True)\n",
"\n",
"run = Run.get_submitted_run()\n",
"\n",
"X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=0)\n",
"data = {\"train\": {\"X\": X_train, \"y\": y_train},\n",
" \"test\": {\"X\": X_test, \"y\": y_test}}\n",
"\n",
"# example of referencing another script\n",
"alphas = mylib.get_alphas()\n",
"\n",
"for alpha in alphas:\n",
" # Use Ridge algorithm to create a regression model\n",
" reg = Ridge(alpha=alpha)\n",
" reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n",
"\n",
" preds = reg.predict(data[\"test\"][\"X\"])\n",
" mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
" run.log('alpha', alpha)\n",
" run.log('mse', mse)\n",
"\n",
" model_file_name='ridge_{0:.2f}.pkl'.format(alpha)\n",
" # save model in the outputs folder so it automatically get uploaded\n",
" with open(model_file_name, \"wb\") as file:\n",
" joblib.dump(value=reg, filename=model_file_name)\n",
" \n",
" # upload the model file explicitly into artifacts \n",
" run.upload_file(name=model_file_name, path_or_stream=model_file_name)\n",
" \n",
" # register the model\n",
" run.register_model(model_name='diabetes-model', model_path=model_file_name)\n",
"\n",
" print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`train.py` also references a `mylib.py` file. So let's create that too."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $script_folder/mylib.py\n",
"import numpy as np\n",
"\n",
"def get_alphas():\n",
" # list of numbers from 0.0 to 1.0 with a 0.05 interval\n",
" return np.arange(0.0, 1.0, 0.05)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure & Run\n",
"### User-managed environment\n",
"Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"\n",
"# Editing a run configuration property on-fly.\n",
"run_config_user_managed = RunConfiguration()\n",
"\n",
"run_config_user_managed.environment.python.user_managed_dependencies = True\n",
"\n",
"# You can choose a specific Python environment by pointing to a Python path \n",
"#run_config.environment.python.interpreter_path = '/home/ninghai/miniconda3/envs/sdk2/bin/python'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Submit script to run in the user-managed environment\n",
"Note whole script folder is submitted for execution, including the `mylib.py` file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=script_folder, script='train.py', run_config=run_config_user_managed)\n",
"run = exp.submit(src)\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get run history details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### System-managed environment\n",
"You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"run_config_system_managed = RunConfiguration()\n",
"\n",
"run_config_system_managed.environment.python.user_managed_dependencies = False\n",
"run_config_system_managed.prepare_environment = True\n",
"\n",
"# Specify conda dependencies with scikit-learn\n",
"cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
"run_config_system_managed.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Submit script to run in the system-managed environment\n",
"A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"src = ScriptRunConfig(source_directory=script_folder, script='train.py', run_config=run_config_system_managed)\n",
"run = exp.submit(src)\n",
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get run history details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Query run metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history",
"get metrics"
]
},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"run.get_metrics()\n",
"metrics = run.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's find the model that has the lowest MSE value logged."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
"\n",
"print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
" min(metrics['mse']), \n",
" best_alpha\n",
"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also list all the files that are associated with this run record"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_file_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# supply a model name, and the full path to the serialized model file.\n",
"model = run.register_model(model_name='best_ridge_model', model_path='ridge_0.40.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(model.name, model.version, model.url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can deploy this model following the example in the 01 notebook."
]
}
],
"metadata": {
"celltoolbar": "Edit Metadata",
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

45
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from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from azureml.core.run import Run
from sklearn.externals import joblib
import numpy as np
# os.makedirs('./outputs', exist_ok = True)
X, y = load_diabetes(return_X_y=True)
run = Run.get_submitted_run()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
data = {"train": {"X": X_train, "y": y_train},
"test": {"X": X_test, "y": y_test}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = np.arange(0.0, 1.0, 0.05)
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
preds = reg.predict(data["test"]["X"])
mse = mean_squared_error(preds, data["test"]["y"])
run.log('alpha', alpha)
run.log('mse', mse)
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
# save model in the outputs folder so it automatically get uploaded
with open(model_file_name, "wb") as file:
joblib.dump(value=reg, filename=model_file_name)
# upload the model file explicitly into artifacts
run.upload_file(name=model_file_name, path_or_stream=model_file_name)
# register the model
# commented out for now until a bug is fixed
# run.register_model(file_name = model_file_name)
print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

342
00.Getting Started/03.train-on-aci/03.train-on-aci.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 03. Train on Azure Container Instance (EXPERIMENTAL)\n",
"\n",
"* Create Workspace\n",
"* Create Project\n",
"* Create `train.py` in the project folder.\n",
"* Configure an ACI (Azure Container Instance) run\n",
"* Execute in ACI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create An Experiment\n",
"\n",
"**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"experiment_name = 'train-on-aci'\n",
"experiment = Experiment(workspace = ws, name = experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a folder to store the training script."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"script_folder = './samples/train-on-aci'\n",
"os.makedirs(script_folder, exist_ok = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Remote execution on ACI\n",
"\n",
"Use `%%writefile` magic to write training code to `train.py` file under the project folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $script_folder/train.py\n",
"\n",
"import os\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"from azureml.core.run import Run\n",
"from sklearn.externals import joblib\n",
"\n",
"import numpy as np\n",
"\n",
"os.makedirs('./outputs', exist_ok=True)\n",
"\n",
"X, y = load_diabetes(return_X_y = True)\n",
"\n",
"run = Run.get_submitted_run()\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
"data = {\"train\": {\"X\": X_train, \"y\": y_train},\n",
" \"test\": {\"X\": X_test, \"y\": y_test}}\n",
"\n",
"# list of numbers from 0.0 to 1.0 with a 0.05 interval\n",
"alphas = np.arange(0.0, 1.0, 0.05)\n",
"\n",
"for alpha in alphas:\n",
" # Use Ridge algorithm to create a regression model\n",
" reg = Ridge(alpha = alpha)\n",
" reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n",
"\n",
" preds = reg.predict(data[\"test\"][\"X\"])\n",
" mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
" run.log('alpha', alpha)\n",
" run.log('mse', mse)\n",
" \n",
" model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n",
" with open(model_file_name, \"wb\") as file:\n",
" joblib.dump(value = reg, filename = 'outputs/' + model_file_name)\n",
"\n",
" print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure for using ACI\n",
"Linux-based ACI is available in `westus`, `eastus`, `westeurope`, `northeurope`, `westus2` and `southeastasia` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"configure run"
]
},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new runconfig object\n",
"run_config = RunConfiguration()\n",
"\n",
"# signal that you want to use ACI to execute script.\n",
"run_config.target = \"containerinstance\"\n",
"\n",
"# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
"run_config.container_instance.region = 'eastus'\n",
"\n",
"# set the ACI CPU and Memory \n",
"run_config.container_instance.cpu_cores = 1\n",
"run_config.container_instance.memory_gb = 2\n",
"\n",
"# enable Docker \n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# set Docker base image to the default CPU-based image\n",
"run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"#run_config.environment.docker.base_image = 'microsoft/mmlspark:plus-0.9.9'\n",
"\n",
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
"run_config.auto_prepare_environment = True\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit the Experiment\n",
"Finally, run the training job on the ACI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"remote run",
"aci"
]
},
"outputs": [],
"source": [
"%%time \n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"\n",
"script_run_config = ScriptRunConfig(source_directory = script_folder,\n",
" script= 'train.py',\n",
" run_config = run_config)\n",
"\n",
"run = experiment.submit(script_run_config)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"remote run",
"aci"
]
},
"outputs": [],
"source": [
"%%time\n",
"# Shows output of the run on stdout.\n",
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history"
]
},
"outputs": [],
"source": [
"# Show run details\n",
"\n",
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Navigate to the above URL using Chrome, and you should see a graph of alpha values, and a graph of MSE.\n",
"\n",
"![graphs](../images/mse.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"get metrics"
]
},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"run.get_metrics()\n",
"metrics = run.get_metrics()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
" min(metrics['mse']), \n",
" metrics['alpha'][np.argmin(metrics['mse'])]\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 04. Train in a remote VM (MLC managed DSVM)\n",
"* Create Workspace\n",
"* Create Project\n",
"* Create `train.py` file\n",
"* Create DSVM as Machine Learning Compute (MLC) resource\n",
"* Configure & execute a run in a conda environment in the default miniconda Docker container on DSVM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'train-on-remote-vm'\n",
"script_folder = './samples/train-on-remote-vm'\n",
"\n",
"import os\n",
"os.makedirs(script_folder, exist_ok = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"exp = Experiment(workspace = ws, name = experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create `train.py`\n",
"\n",
"Use `%%writefile` magic to write training code to `train.py` file under your project folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $script_folder/train.py\n",
"\n",
"import os\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"from azureml.core.run import Run\n",
"from sklearn.externals import joblib\n",
"\n",
"import numpy as np\n",
"\n",
"os.makedirs('./outputs', exist_ok=True)\n",
"\n",
"X, y = load_diabetes(return_X_y = True)\n",
"\n",
"run = Run.get_submitted_run()\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
"data = {\"train\": {\"X\": X_train, \"y\": y_train},\n",
" \"test\": {\"X\": X_test, \"y\": y_test}}\n",
"\n",
"# list of numbers from 0.0 to 1.0 with a 0.05 interval\n",
"alphas = np.arange(0.0, 1.0, 0.05)\n",
"\n",
"for alpha in alphas:\n",
" # Use Ridge algorithm to create a regression model\n",
" reg = Ridge(alpha = alpha)\n",
" reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n",
"\n",
" preds = reg.predict(data[\"test\"][\"X\"])\n",
" mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
" run.log('alpha', alpha)\n",
" run.log('mse', mse)\n",
" \n",
" model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n",
" with open(model_file_name, \"wb\") as file:\n",
" joblib.dump(value = reg, filename = 'outputs/' + model_file_name)\n",
"\n",
" print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Linux DSVM as a compute target\n",
"\n",
"**Note**: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
" \n",
"**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can append the port number to the address like the example below. [Read more](../../documentation/sdk/ssh-issue.md) on this."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import DsvmCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"compute_target_name = 'mydsvm'\n",
"\n",
"try:\n",
" dsvm_compute = DsvmCompute(workspace = ws, name = compute_target_name)\n",
" print('found existing:', dsvm_compute.name)\n",
"except ComputeTargetException:\n",
" print('creating new.')\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
" dsvm_compute = DsvmCompute.create(ws, name = compute_target_name, provisioning_configuration = dsvm_config)\n",
" dsvm_compute.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure & Run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure a Docker run with new conda environment on the VM\n",
"You can execute in a Docker container in the VM. If you choose this route, you don't need to install anything on the VM yourself. Azure ML execution service will take care of it for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"\n",
"# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
"run_config = RunConfiguration(framework = \"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"run_config.target = compute_target_name\n",
"\n",
"# Use Docker in the remote VM\n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# Use CPU base image from DockerHub\n",
"run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"print('Base Docker image is:', run_config.environment.docker.base_image)\n",
"\n",
"# Ask system to provision a new one based on the conda_dependencies.yml file\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
"run_config.prepare_environment = True\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit the Experiment\n",
"Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Run\n",
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory = script_folder, script = 'train.py', run_config = run_config)\n",
"run = exp.submit(src)\n",
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View run history details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find the best run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"run.get_metrics()\n",
"metrics = run.get_metrics()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
" min(metrics['mse']), \n",
" metrics['alpha'][np.argmin(metrics['mse'])]\n",
"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clean up compute resource"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dsvm_compute.delete()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

39
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import os
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from azureml.core import Run
from sklearn.externals import joblib
import numpy as np
os.makedirs('./outputs', exist_ok=True)
X, y = load_diabetes(return_X_y=True)
run = Run.get_submitted_run()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
data = {"train": {"X": X_train, "y": y_train},
"test": {"X": X_test, "y": y_test}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = np.arange(0.0, 1.0, 0.05)
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
preds = reg.predict(data["test"]["X"])
mse = mean_squared_error(preds, data["test"]["y"])
run.log('alpha', alpha)
run.log('mse', mse)
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
with open(model_file_name, "wb") as file:
joblib.dump(value=reg, filename='outputs/' + model_file_name)
print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

470
00.Getting Started/05.train-in-spark/05.train-in-spark.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 05. Train in Spark\n",
"* Create Workspace\n",
"* Create Project\n",
"* Create `train-spark.py` file in the project folder\n",
"* Execute a PySpark script in ACI.\n",
"* Execute a PySpark script in a Docker container on remote DSVM\n",
"* Execute a PySpark script in HDI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Project and Associate with Run History\n",
"**Project** is a local folder that contains files for your Azure ML experiments. It is associated with a **run history**, a cloud container of run metrics and output artifacts from your experiments. You can either attach a local folder as a new project, or load a local folder as a project if it has been attached before."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'train-on-spark'\n",
"\n",
"# project folder\n",
"project_folder = './sample_projects/train-on-spark'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from azureml.project.project import Project\n",
"\n",
"project = Project.attach(workspace_object = ws,\n",
" experiment_name = experiment_name,\n",
" directory = project_folder)\n",
"\n",
"print(project.project_directory, project.history.name, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Copy files\n",
"\n",
"\n",
"Copy `train-spark.py` and `iris.csv` into the project folde"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from shutil import copyfile\n",
"\n",
"# copy iris dataset in to project folder\n",
"copyfile('./iris.csv', os.path.join(project_folder, 'iris.csv'))\n",
"\n",
"# copy train-spark.py file into project folder\n",
"# train-spark.py trains a simple LogisticRegression model using Spark.ML algorithm\n",
"copyfile('./train-spark.py', os.path.join(project_folder, 'train-spark.py'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Review the train-spark.py file in the project folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(os.path.join(project_folder, 'train-spark.py'), 'r') as fin:\n",
" print(fin.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure & Run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure ACI target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new runconfig object\n",
"run_config = RunConfiguration()\n",
"\n",
"# signal that you want to use ACI to execute script.\n",
"run_config.target = \"containerinstance\"\n",
"\n",
"# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
"run_config.container_instance.region = 'eastus'\n",
"\n",
"# set the ACI CPU and Memory \n",
"run_config.container_instance.cpu_cores = 1\n",
"run_config.container_instance.memory_gb = 2\n",
"\n",
"# enable Docker \n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# set Docker base image to the default CPU-based image\n",
"run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
"print('base image is', run_config.environment.docker.base_image)\n",
"#run_config.environment.docker.base_image = 'microsoft/mmlspark:plus-0.9.9'\n",
"\n",
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
"# please update this file if you need additional packages.\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
"run_config.auto_prepare_environment = True\n",
"\n",
"cd = CondaDependencies()\n",
"# add numpy as a dependency\n",
"cd.add_conda_package('numpy')\n",
"# overwrite the default conda_dependencies.yml file\n",
"cd.save_to_file(base_directory = project_folder, conda_file_path='aml_config/conda_dependencies.yml')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Spark job in ACI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time \n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"\n",
"experiment = Experiment(project_object.workspace_object, project_object.history.name)\n",
"script_run_config = ScriptRunConfig(source_directory = project.project_directory,\n",
" script= 'train-spark.py',\n",
" run_config = run_config)\n",
"run = experiment.submit(script_run_config)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show the run in the web UI\n",
"**IMPORTANT**: Please use Chrome to navigate to the URL."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# import helpers.py\n",
"import helpers\n",
"\n",
"# get the URL of the run history web page\n",
"print(helpers.get_run_history_url(run))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Attach a remote Linux VM\n",
"To use remote docker commpute target:\n",
" 1. Create a Linux DSVM in Azure. Here is some [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
" 2. Enter the IP address, username and password below\n",
" \n",
"**Note**: the below example use port 5022. By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can append the port number to the address like the example below. [Read more](../../documentation/sdk/ssh-issue.md) on this."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import RemoteCompute\n",
"\n",
"try:\n",
" # Attaches a remote docker on a remote vm as a compute target.\n",
" RemoteCompute.attach(workspace,name = \"cpu-dsvm\", username = \"ninghai\", \n",
" address = \"hai2.eastus2.cloudapp.azure.com:5022\", \n",
" ssh-port=22\n",
" password = \"<password>\"))\n",
"except UserErrorException as e:\n",
" print(\"Caught = {}\".format(e.message))\n",
" print(\"Compute config already attached.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure a Spark Docker run on the VM\n",
"Execute in the Spark engine in a Docker container in the VM. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
"run_config = RunConfiguration.load(path = project_folder, name = \"cpu-dsvm\")\n",
"\n",
"# set framework to PySpark\n",
"run_config.framework = \"PySpark\"\n",
"\n",
"# Use Docker in the remote VM\n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# Use the MMLSpark CPU based image.\n",
"# https://hub.docker.com/r/microsoft/mmlspark/\n",
"run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
"print('base image is:', run_config.environment.docker.base_image)\n",
"\n",
"# signal use the user-managed environment\n",
"# do NOT provision a new one based on the conda.yml file\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# Prepare the Docker and conda environment automatically when execute for the first time.\n",
"run_config.auto_prepare_environment = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit the Experiment\n",
"Submit script to run in the Spark engine in the Docker container in the remote VM."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"script_run_config = ScriptRunConfig(source_directory = project.project_directory,\n",
" script= 'train-spark.py',\n",
" run_config = run_config)\n",
"run = experiment.submit(script_run_config)\n",
"\n",
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get the URL of the run history web page\n",
"print(helpers.get_run_history_url(run))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Attach an HDI cluster\n",
"To use HDI commpute target:\n",
" 1. Create an Spark for HDI cluster in Azure. Here is some [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
" 2. Enter the IP address, username and password below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import HDInsightCompute\n",
"\n",
"try:\n",
" # Attaches a HDI cluster as a compute target.\n",
" HDInsightCompute.attach(ws, name = \"myhdi\",\n",
" username = \"ninghai\", \n",
" address = \"sparkhai-ssh.azurehdinsight.net\", \n",
" password = \"<pwd>\"))\n",
"except UserErrorException as e:\n",
" print(\"Caught = {}\".format(e.message))\n",
" print(\"Compute config already attached.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure HDI run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load the runconfig object from the \"myhdi.runconfig\" file generated by the attach operaton above.\n",
"run_config = RunConfiguration.load(path = project_folder, name = 'myhdi')\n",
"\n",
"# ask system to prepare the conda environment automatically when executed for the first time\n",
"run_config.auto_prepare_environment = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit the script to HDI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"script_run_config = ScriptRunConfig(source_directory = project.project_directory,\n",
" script= 'train-spark.py',\n",
" run_config = run_config)\n",
"run = experiment.submit(script_run_config)\n",
"\n",
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get the URL of the run history web page\n",
"print(helpers.get_run_history_url(run))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"metrics = run.get_metrics()\n",
"print(metrics)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

150
00.Getting Started/05.train-in-spark/iris.csv Normal file
View File

@@ -0,0 +1,150 @@
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
1 5.1 3.5 1.4 0.2 Iris-setosa
2 4.9 3.0 1.4 0.2 Iris-setosa
3 4.7 3.2 1.3 0.2 Iris-setosa
4 4.6 3.1 1.5 0.2 Iris-setosa
5 5.0 3.6 1.4 0.2 Iris-setosa
6 5.4 3.9 1.7 0.4 Iris-setosa
7 4.6 3.4 1.4 0.3 Iris-setosa
8 5.0 3.4 1.5 0.2 Iris-setosa
9 4.4 2.9 1.4 0.2 Iris-setosa
10 4.9 3.1 1.5 0.1 Iris-setosa
11 5.4 3.7 1.5 0.2 Iris-setosa
12 4.8 3.4 1.6 0.2 Iris-setosa
13 4.8 3.0 1.4 0.1 Iris-setosa
14 4.3 3.0 1.1 0.1 Iris-setosa
15 5.8 4.0 1.2 0.2 Iris-setosa
16 5.7 4.4 1.5 0.4 Iris-setosa
17 5.4 3.9 1.3 0.4 Iris-setosa
18 5.1 3.5 1.4 0.3 Iris-setosa
19 5.7 3.8 1.7 0.3 Iris-setosa
20 5.1 3.8 1.5 0.3 Iris-setosa
21 5.4 3.4 1.7 0.2 Iris-setosa
22 5.1 3.7 1.5 0.4 Iris-setosa
23 4.6 3.6 1.0 0.2 Iris-setosa
24 5.1 3.3 1.7 0.5 Iris-setosa
25 4.8 3.4 1.9 0.2 Iris-setosa
26 5.0 3.0 1.6 0.2 Iris-setosa
27 5.0 3.4 1.6 0.4 Iris-setosa
28 5.2 3.5 1.5 0.2 Iris-setosa
29 5.2 3.4 1.4 0.2 Iris-setosa
30 4.7 3.2 1.6 0.2 Iris-setosa
31 4.8 3.1 1.6 0.2 Iris-setosa
32 5.4 3.4 1.5 0.4 Iris-setosa
33 5.2 4.1 1.5 0.1 Iris-setosa
34 5.5 4.2 1.4 0.2 Iris-setosa
35 4.9 3.1 1.5 0.1 Iris-setosa
36 5.0 3.2 1.2 0.2 Iris-setosa
37 5.5 3.5 1.3 0.2 Iris-setosa
38 4.9 3.1 1.5 0.1 Iris-setosa
39 4.4 3.0 1.3 0.2 Iris-setosa
40 5.1 3.4 1.5 0.2 Iris-setosa
41 5.0 3.5 1.3 0.3 Iris-setosa
42 4.5 2.3 1.3 0.3 Iris-setosa
43 4.4 3.2 1.3 0.2 Iris-setosa
44 5.0 3.5 1.6 0.6 Iris-setosa
45 5.1 3.8 1.9 0.4 Iris-setosa
46 4.8 3.0 1.4 0.3 Iris-setosa
47 5.1 3.8 1.6 0.2 Iris-setosa
48 4.6 3.2 1.4 0.2 Iris-setosa
49 5.3 3.7 1.5 0.2 Iris-setosa
50 5.0 3.3 1.4 0.2 Iris-setosa
51 7.0 3.2 4.7 1.4 Iris-versicolor
52 6.4 3.2 4.5 1.5 Iris-versicolor
53 6.9 3.1 4.9 1.5 Iris-versicolor
54 5.5 2.3 4.0 1.3 Iris-versicolor
55 6.5 2.8 4.6 1.5 Iris-versicolor
56 5.7 2.8 4.5 1.3 Iris-versicolor
57 6.3 3.3 4.7 1.6 Iris-versicolor
58 4.9 2.4 3.3 1.0 Iris-versicolor
59 6.6 2.9 4.6 1.3 Iris-versicolor
60 5.2 2.7 3.9 1.4 Iris-versicolor
61 5.0 2.0 3.5 1.0 Iris-versicolor
62 5.9 3.0 4.2 1.5 Iris-versicolor
63 6.0 2.2 4.0 1.0 Iris-versicolor
64 6.1 2.9 4.7 1.4 Iris-versicolor
65 5.6 2.9 3.6 1.3 Iris-versicolor
66 6.7 3.1 4.4 1.4 Iris-versicolor
67 5.6 3.0 4.5 1.5 Iris-versicolor
68 5.8 2.7 4.1 1.0 Iris-versicolor
69 6.2 2.2 4.5 1.5 Iris-versicolor
70 5.6 2.5 3.9 1.1 Iris-versicolor
71 5.9 3.2 4.8 1.8 Iris-versicolor
72 6.1 2.8 4.0 1.3 Iris-versicolor
73 6.3 2.5 4.9 1.5 Iris-versicolor
74 6.1 2.8 4.7 1.2 Iris-versicolor
75 6.4 2.9 4.3 1.3 Iris-versicolor
76 6.6 3.0 4.4 1.4 Iris-versicolor
77 6.8 2.8 4.8 1.4 Iris-versicolor
78 6.7 3.0 5.0 1.7 Iris-versicolor
79 6.0 2.9 4.5 1.5 Iris-versicolor
80 5.7 2.6 3.5 1.0 Iris-versicolor
81 5.5 2.4 3.8 1.1 Iris-versicolor
82 5.5 2.4 3.7 1.0 Iris-versicolor
83 5.8 2.7 3.9 1.2 Iris-versicolor
84 6.0 2.7 5.1 1.6 Iris-versicolor
85 5.4 3.0 4.5 1.5 Iris-versicolor
86 6.0 3.4 4.5 1.6 Iris-versicolor
87 6.7 3.1 4.7 1.5 Iris-versicolor
88 6.3 2.3 4.4 1.3 Iris-versicolor
89 5.6 3.0 4.1 1.3 Iris-versicolor
90 5.5 2.5 4.0 1.3 Iris-versicolor
91 5.5 2.6 4.4 1.2 Iris-versicolor
92 6.1 3.0 4.6 1.4 Iris-versicolor
93 5.8 2.6 4.0 1.2 Iris-versicolor
94 5.0 2.3 3.3 1.0 Iris-versicolor
95 5.6 2.7 4.2 1.3 Iris-versicolor
96 5.7 3.0 4.2 1.2 Iris-versicolor
97 5.7 2.9 4.2 1.3 Iris-versicolor
98 6.2 2.9 4.3 1.3 Iris-versicolor
99 5.1 2.5 3.0 1.1 Iris-versicolor
100 5.7 2.8 4.1 1.3 Iris-versicolor
101 6.3 3.3 6.0 2.5 Iris-virginica
102 5.8 2.7 5.1 1.9 Iris-virginica
103 7.1 3.0 5.9 2.1 Iris-virginica
104 6.3 2.9 5.6 1.8 Iris-virginica
105 6.5 3.0 5.8 2.2 Iris-virginica
106 7.6 3.0 6.6 2.1 Iris-virginica
107 4.9 2.5 4.5 1.7 Iris-virginica
108 7.3 2.9 6.3 1.8 Iris-virginica
109 6.7 2.5 5.8 1.8 Iris-virginica
110 7.2 3.6 6.1 2.5 Iris-virginica
111 6.5 3.2 5.1 2.0 Iris-virginica
112 6.4 2.7 5.3 1.9 Iris-virginica
113 6.8 3.0 5.5 2.1 Iris-virginica
114 5.7 2.5 5.0 2.0 Iris-virginica
115 5.8 2.8 5.1 2.4 Iris-virginica
116 6.4 3.2 5.3 2.3 Iris-virginica
117 6.5 3.0 5.5 1.8 Iris-virginica
118 7.7 3.8 6.7 2.2 Iris-virginica
119 7.7 2.6 6.9 2.3 Iris-virginica
120 6.0 2.2 5.0 1.5 Iris-virginica
121 6.9 3.2 5.7 2.3 Iris-virginica
122 5.6 2.8 4.9 2.0 Iris-virginica
123 7.7 2.8 6.7 2.0 Iris-virginica
124 6.3 2.7 4.9 1.8 Iris-virginica
125 6.7 3.3 5.7 2.1 Iris-virginica
126 7.2 3.2 6.0 1.8 Iris-virginica
127 6.2 2.8 4.8 1.8 Iris-virginica
128 6.1 3.0 4.9 1.8 Iris-virginica
129 6.4 2.8 5.6 2.1 Iris-virginica
130 7.2 3.0 5.8 1.6 Iris-virginica
131 7.4 2.8 6.1 1.9 Iris-virginica
132 7.9 3.8 6.4 2.0 Iris-virginica
133 6.4 2.8 5.6 2.2 Iris-virginica
134 6.3 2.8 5.1 1.5 Iris-virginica
135 6.1 2.6 5.6 1.4 Iris-virginica
136 7.7 3.0 6.1 2.3 Iris-virginica
137 6.3 3.4 5.6 2.4 Iris-virginica
138 6.4 3.1 5.5 1.8 Iris-virginica
139 6.0 3.0 4.8 1.8 Iris-virginica
140 6.9 3.1 5.4 2.1 Iris-virginica
141 6.7 3.1 5.6 2.4 Iris-virginica
142 6.9 3.1 5.1 2.3 Iris-virginica
143 5.8 2.7 5.1 1.9 Iris-virginica
144 6.8 3.2 5.9 2.3 Iris-virginica
145 6.7 3.3 5.7 2.5 Iris-virginica
146 6.7 3.0 5.2 2.3 Iris-virginica
147 6.3 2.5 5.0 1.9 Iris-virginica
148 6.5 3.0 5.2 2.0 Iris-virginica
149 6.2 3.4 5.4 2.3 Iris-virginica
150 5.9 3.0 5.1 1.8 Iris-virginica

92
00.Getting Started/05.train-in-spark/train-spark.py Normal file
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@@ -0,0 +1,92 @@
import numpy as np
import pyspark
import os
import urllib
import sys
from pyspark.sql.functions import *
from pyspark.ml.classification import *
from pyspark.ml.evaluation import *
from pyspark.ml.feature import *
from pyspark.sql.types import StructType, StructField
from pyspark.sql.types import DoubleType, IntegerType, StringType
from azureml.core.run import Run
# initialize logger
run = Run.get_submitted_run()
# start Spark session
spark = pyspark.sql.SparkSession.builder.appName('Iris').getOrCreate()
# print runtime versions
print('****************')
print('Python version: {}'.format(sys.version))
print('Spark version: {}'.format(spark.version))
print('****************')
# load iris.csv into Spark dataframe
schema = StructType([
StructField("sepal-length", DoubleType()),
StructField("sepal-width", DoubleType()),
StructField("petal-length", DoubleType()),
StructField("petal-width", DoubleType()),
StructField("class", StringType())
])
data = spark.read.csv('iris.csv', header=False, schema=schema)
print("First 10 rows of Iris dataset:")
data.show(10)
# vectorize all numerical columns into a single feature column
feature_cols = data.columns[:-1]
assembler = pyspark.ml.feature.VectorAssembler(
inputCols=feature_cols, outputCol='features')
data = assembler.transform(data)
# convert text labels into indices
data = data.select(['features', 'class'])
label_indexer = pyspark.ml.feature.StringIndexer(
inputCol='class', outputCol='label').fit(data)
data = label_indexer.transform(data)
# only select the features and label column
data = data.select(['features', 'label'])
print("Reading for machine learning")
data.show(10)
# change regularization rate and you will likely get a different accuracy.
reg = 0.01
# load regularization rate from argument if present
if len(sys.argv) > 1:
reg = float(sys.argv[1])
# log regularization rate
run.log("Regularization Rate", reg)
# use Logistic Regression to train on the training set
train, test = data.randomSplit([0.70, 0.30])
lr = pyspark.ml.classification.LogisticRegression(regParam=reg)
model = lr.fit(train)
# predict on the test set
prediction = model.transform(test)
print("Prediction")
prediction.show(10)
# evaluate the accuracy of the model using the test set
evaluator = pyspark.ml.evaluation.MulticlassClassificationEvaluator(
metricName='accuracy')
accuracy = evaluator.evaluate(prediction)
print()
print('#####################################')
print('Regularization rate is {}'.format(reg))
print("Accuracy is {}".format(accuracy))
print('#####################################')
print()
# log accuracy
run.log('Accuracy', accuracy)

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from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
import os
import argparse
# Import Run from azureml.core,
from azureml.core.run import Run
parser = argparse.ArgumentParser()
parser.add_argument('--alpha', type=float, dest='alpha',
default=0.5, help='regularization strength')
args = parser.parse_args()
# Get handle of current run for logging and history purposes
run = Run.get_submitted_run()
X, y = load_diabetes(return_X_y=True)
columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
x_train, x_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=0)
data = {"train": {"x": x_train, "y": y_train},
"test": {"x": x_test, "y": y_test}}
alpha = args.alpha
print('alpha value is:', alpha)
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["x"], data["train"]["y"])
print('Ridget model fitted.')
preds = reg.predict(data["test"]["x"])
mse = mean_squared_error(preds, data["test"]["y"])
# Log metrics
run.log("alpha", alpha)
run.log("mse", mse)
os.makedirs('./outputs', exist_ok=True)
model_file_name = "model.pkl"
# Save model as part of the run history
with open(model_file_name, "wb") as file:
joblib.dump(reg, 'outputs/' + model_file_name)
print('Mean Squared Error is:', mse)

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from __future__ import print_function
import tensorflow as tf
import numpy as np
import os
import json
import base64
from io import BytesIO
from PIL import Image
##############################################
# helper functions
##############################################
def build_model(x, y_, keep_prob):
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
return y_conv
def base64ToImg(base64ImgString):
if base64ImgString.startswith('b\''):
base64ImgString = base64ImgString[2:-1]
base64Img = base64ImgString.encode('utf-8')
decoded_img = base64.b64decode(base64Img)
img_buffer = BytesIO(decoded_img)
img = Image.open(img_buffer)
return img
##############################################
# API init() and run() methods
##############################################
def init():
global x, keep_prob, y_conv, sess
g = tf.Graph()
with g.as_default():
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
keep_prob = tf.placeholder(tf.float32)
y_conv = build_model(x, y_, keep_prob)
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
model_dir = os.path.join('sample_projects', 'outputs')
saved_model_path = os.path.join(model_dir, 'model.ckpt')
sess = tf.Session(graph=g)
sess.run(init_op)
saver.restore(sess, saved_model_path)
def run(input_data):
img = base64ToImg(json.loads(input_data)['data'])
img_data = np.array(img, dtype=np.float32).flatten()
img_data.resize((1, 784))
y_pred = sess.run(y_conv, feed_dict={x: img_data, keep_prob: 1.0})
predicted_label = np.argmax(y_pred[0])
outJsonString = json.dumps({"label": str(predicted_label)})
return str(outJsonString)

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
# Load MNIST Data
from tensorflow.examples.tutorials.mnist import input_data
import os
import argparse
from azureml.core.run import Run
# the following 10 lines can be removed once BUG# 241943 is fixed
def get_logger():
try:
return Run.get_submitted_run()
except Exception:
return LocalLogger()
class LocalLogger:
def log(self, key, value):
print("AML-Log:", key, value)
def build_model(x, y_, keep_prob):
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
return y_conv
def main():
# Get command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--learning_rate', type=float,
default=0.0001, help='learning rate')
parser.add_argument('--minibatch_size', type=int,
default=50, help='minibatch size')
parser.add_argument('--keep_probability', type=float,
default=0.5, help='keep probability for dropout layer')
parser.add_argument('--num_iterations', type=int,
default=1000, help='number of iterations')
parser.add_argument('--output_dir', type=str, default='./outputs',
help='output directory to write checkpoints to')
args = parser.parse_args()
# log parameters
run_logger = get_logger()
run_logger.log("learning_rate", args.learning_rate)
run_logger.log("minibatch_size", args.minibatch_size)
run_logger.log("keep_probability", args.keep_probability)
run_logger.log("num_iterations", args.num_iterations)
# Load MNIST data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
keep_prob = tf.placeholder(tf.float32)
y_conv = build_model(x, y_, keep_prob)
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(
args.learning_rate).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.global_variables_initializer())
for i in range(args.num_iterations):
batch = mnist.train.next_batch(args.minibatch_size)
if i % 100 == 0:
test_acc = accuracy.eval(
feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
train_accuracy = accuracy.eval(
feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g, test accuracy, %g" %
(i, train_accuracy, test_acc))
# log test accuracy to AML
run_logger.log("Accuracy", float(test_acc))
run_logger.log("Iterations", i)
sess.run(train_step, feed_dict={
x: batch[0], y_: batch[1], keep_prob: args.keep_probability})
# Save the trained model
model_dir = args.output_dir
model_file = 'model.ckpt'
os.makedirs(model_dir, exist_ok=True)
saver = tf.train.Saver()
saver.save(sess, os.path.join(model_dir, model_file))
final_test_acc = sess.run(accuracy, feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
run_logger.log("Accuracy", float(final_test_acc))
run_logger.log("Iterations", args.num_iterations)
print("test accuracy %g" % final_test_acc)
if __name__ == "__main__":
main()

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00.Getting Started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10. Register Model, Create Image and Deploy Service\n",
"\n",
"This example shows how to deploy a web service in step-by-step fashion:\n",
"\n",
" 1. Register model\n",
" 2. Query versions of models and select one to deploy\n",
" 3. Create Docker image\n",
" 4. Query versions of images\n",
" 5. Deploy the image as web service\n",
" \n",
"**IMPORTANT**:\n",
" * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
" \n",
"The 101 Notebook taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Register Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
"\n",
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from file"
]
},
"outputs": [],
"source": [
"from azureml.core.model import Model\n",
"import sklearn\n",
"\n",
"library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
"\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
" model_name = \"sklearn_regression_model.pkl\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from file"
]
},
"outputs": [],
"source": [
"regression_models = ws.models(tags=['area'])\n",
"for m in regression_models:\n",
" print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can pick a specific model to deploy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(model.name, model.description, model.version, sep = '\\t')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Docker Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"\n",
"def init():\n",
" global model\n",
" # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
" # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
" model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"result\": result.tolist()})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that following command can take few minutes. \n",
"\n",
"You can add tags and descriptions to images. Also, an image can contain multiple models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create image"
]
},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script=\"score.py\",\n",
" conda_file=\"myenv.yml\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Image with ridge regression model\")\n",
"\n",
"image = Image.create(name = \"myimage1\",\n",
" # this is the model object \n",
" models = [model],\n",
" image_config = image_config, \n",
" workspace = ws)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create image"
]
},
"outputs": [],
"source": [
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List images by tag and find out the detailed build log for debugging."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create image"
]
},
"outputs": [],
"source": [
"for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
" print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy image as web service on Azure Container Instance\n",
"\n",
"Note that the service creation can take few minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
" description = 'Predict diabetes using regression model')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'my-aci-service-2'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test web service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call the web service with some dummy input data to get a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,2,3,4,5,6,7,8,9,10], \n",
" [10,9,8,7,6,5,4,3,2,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"prediction = aci_service.run(input_data = test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete ACI to clean up"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"aci_service.delete()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
"This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
"We then test and delete the service, image and model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"print(azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get workspace\n",
"Load existing workspace from the config file info."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.workspace import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register the model\n",
"Register an existing trained model, add descirption and tags."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
" model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)\n",
"\n",
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create an image\n",
"Create an image using the registered model the script that will load and run the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"\n",
"def init():\n",
" global model\n",
" # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
" # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
" model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"result\": result.tolist()})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" description = \"Image with ridge regression model\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"myimage1\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Provision the AKS Cluster\n",
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use the default configuration (can also provide parameters to customize)\n",
"prov_config = AksCompute.provisioning_configuration()\n",
"\n",
"aks_name = 'my-aks-9' \n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optional step: Attach existing AKS cluster\n",
"\n",
"If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Use the default configuration (can also provide parameters to customize)\n",
"resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
"\n",
"create_name='my-existing-aks' \n",
"# Create the cluster\n",
"aks_target = AksCompute.attach(workspace=ws, name=create_name, resource_id=resource_id)\n",
"# Wait for the operation to complete\n",
"aks_target.wait_for_completion(True)\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy web service to AKS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration (using default here)\n",
"aks_config = AksWebservice.deploy_configuration()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service_name ='aks-service-1'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test the web service\n",
"We test the web sevice by passing data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,2,3,4,5,6,7,8,9,10], \n",
" [10,9,8,7,6,5,4,3,2,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"prediction = aks_service.run(input_data = test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clean up\n",
"Delete the service, image and model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service.delete()\n",
"image.delete()\n",
"model.delete()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Enabling Data Collection for Models in Production\n",
"With this notebook, you can learn how to collect input model data from your Azure Machine Learning service in an Azure Blob storage. Once enabled, this data collected gives you the opportunity:\n",
"\n",
"* Monitor data drifts as production data enters your model\n",
"* Make better decisions on when to retrain or optimize your model\n",
"* Retrain your model with the data collected\n",
"\n",
"## What data is collected?\n",
"* Model input data (voice, images, and video are not supported) from services deployed in Azure Kubernetes Cluster (AKS)\n",
"* Model predictions using production input data.\n",
"\n",
"**Note:** pre-aggregation or pre-calculations on this data are done by user and not included in this version of the product.\n",
"\n",
"## What is different compared to standard production deployment process?\n",
"1. Update scoring file.\n",
"2. Update yml file with new dependency.\n",
"3. Update aks configuration.\n",
"4. Build new image and deploy it. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import your dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace, Run\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model\n",
"\n",
"import azureml.core\n",
"print(azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Set up your configuration and create a workspace\n",
"Follow Notebook 00 instructions to do this.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Register Model\n",
"Register an existing trained model, add descirption and tags."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"model = Model.register(model_path = 'sklearn_regression_model.pkl', # this points to a local file\n",
" model_name = \"best_model\", # this is the name the model is registered as\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)\n",
"\n",
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Update your scoring file with Data Collection\n",
"The file below, compared to the file used in notebook 11, has the following changes:\n",
"### a. Import the module\n",
"<code> from azureml.monitoring import ModelDataCollector </code>\n",
"### b. In your init function add:\n",
"<code> global inputs_dc, prediction_d\n",
" inputs_dc = ModelDataCollector(\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\". \"feat4\", \"feat5\", \"Feat6\"])\n",
" prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"])</code>\n",
" \n",
"* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide “raw” data versus “processed”.\n",
"* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn’t require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n",
"* Feature Names: These need to be set up in the order of your features in order for them to have column names when the .csv is created.\n",
"\n",
"### c. In your run function add:\n",
"<code> inputs_dc.collect(data)\n",
" prediction_dc.collect(result) </code>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy as np\n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"from azureml.monitoring import ModelDataCollector\n",
"import time\n",
"\n",
"def init():\n",
" global model\n",
" #print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
" # note here \"best_model\" is the name of the model registered under the workspace\n",
" # this call should return the path to the model.pkl file on the local disk.\n",
" model_path = Model.get_model_path(model_name = 'best_model')\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
" global inputs_dc, prediction_dc\n",
" # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
" inputs_dc = ModelDataCollector(model_name=\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\",\"feat4\", \"feat5\",\"feat6\"]) \n",
" # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
" prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
" \n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" global inputs_dc, prediction_dc\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = np.array(data)\n",
" result = model.predict(data)\n",
" inputs_dc.collect(data) #this call is saving our input data into our blob\n",
" prediction_dc.collect(result)#this call is saving our prediction data into our blob\n",
" return json.dumps({\"result\": result.tolist()})\n",
" except Exception as e:\n",
" result = str(e)\n",
" #print (result + time.strftime(\"%H:%M:%S\"))\n",
" return json.dumps({\"error\": result})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Update your myenv.yml file with the required module"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile myenv.yml\n",
"name: myenv\n",
"channels:\n",
" - defaults\n",
"dependencies:\n",
" - pip:\n",
" - numpy\n",
" - scikit-learn\n",
" # Required packages for AzureML execution, history, and data preparation.\n",
" - --extra-index-url https://azuremlsdktestpypi.azureedge.net/sdk-release/Preview/E7501C02541B433786111FE8E140CAA1\n",
" - azureml-core\n",
" - azureml-monitoring"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Create your new Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" description = \"Image with ridge regression model\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"myimage1\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Deploy to AKS service\n",
"For this step you would need to have an AKS cluster setup (Notebook 11).\n",
"In this case we are attaching to a previously created service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/marthateresource_groupjw/providers/Microsoft.ContainerService/managedClusters/my-aks-colfb348092fd3a760'\n",
"create_name= 'my-existing-aks'\n",
"aks_target = AksCompute.attach(workspace = ws, \n",
" name = create_name, \n",
" resource_id=resource_id)\n",
"# Wait for the operation to complete\n",
"aks_target.wait_for_completion(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Activate Data Collection and App Insights\n",
"In order to enable Data Collection and App Insights in your service you will need to update your AKS configuration file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration (using default here)\n",
"aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Deploy your service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service_name ='aks-w-collv5'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target\n",
" )\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Test your service and send some data\n",
"**Note**: It will take around 15 mins for your data to appear in your blob.\n",
"The data will appear in your Azure Blob following this format:\n",
"\n",
"/modeldata/subscriptionid/resourcegroupname/workspacename/webservicename/modelname/modelversion/identifier/year/month/day/data.csv "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,2,3,4,54,6,7,8,88,10], \n",
" [10,9,8,37,36,45,4,33,2,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"prediction = aks_service.run(input_data = test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_sample = json.dumps({'data': [\n",
" [1,22,3,4,5,68,7,98,95,310], \n",
" [10,92,8,7,6,53,84,23,323,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"prediction = aks_service.run(input_data = test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Validate you data and analyze it\n",
"You can look into your data following this path format in your Azure Blob:\n",
"\n",
"/modeldata/**subscriptionid>**/**resourcegroupname>**/**workspacename>**/**webservicename>**/**modelname>**/**modelversion>>**/**identifier>**/*year/month/day*/data.csv \n",
"\n",
"For doing further analysis you have multiple options:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Create DataBricks cluster and connect it to your blob\n",
"https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal or in your databricks workspace you can look for the template \"Azure Blob Storage Import Example Notebook\".\n",
"\n",
"\n",
"Here is an example for setting up the file location to extract the relevant data:\n",
"\n",
"<code> file_location = \"wasbs://mycontainer@testmartstoragendbblgwy.blob.core.windows.net/unknown/unknown/unknown-bigdataset-unknown/my_iterate_parking_inputs/2018/&deg;/&deg;/data.csv\" \n",
"file_type = \"csv\"</code>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Connect Blob to Power Bi (Small Data only)\n",
"1. Download and Open PowerBi Desktop\n",
"2. Select “Get Data” and click on “Azure Blob Storage” >> Connect\n",
"3. Add your storage account and enter your storage key.\n",
"4. Select the container where your Data Collection is stored and click on Edit. \n",
"5. In the query editor, click under “Name” column and add your Storage account Model path into the filter. Note: if you want to only look into files from a specific year or month, just expand the filter path. For example, just look into March data: /modeldata/subscriptionid>/resourcegroupname>/workspacename>/webservicename>/modelname>/modelversion>/identifier>/year>/3\n",
"6. Click on the double arrow aside the “Content” column to combine the files. \n",
"7. Click OK and the data will preload.\n",
"8. You can now click Close and Apply and start building your custom reports on your Model Input data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Disable Data Collection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_service.update(collect_model_data=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
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00.Getting Started/12.enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl Normal file
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